1
A Brief Introduction and Importance of Data Science

Karthika N.1*, Sheela J.1 and Janet B.2

1Department of SCOPE, VIT-AP University, Amaravati, Andhra Pradesh, India

2Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India

Abstract

Data is very important component of any organization. According to International Data Corporation, by 2025, global data will reach to 175 zettabytes. They need data to help them make careful decisions in business. Data is worthless until it is transformed into valuable data. Data science plays a vital role in processing and interpreting data. It focuses on the analysis and management of data too. It is concerned with obtaining useful information from large datasets. It is frequently applied in a wide range of industries, including healthcare, marketing, banking, finance, policy work, and more. This enables companies to make informed decisions around growth, optimization, and performance. In this brief monograph, we address following questions.

What is data science and what does a data scientist do? Why data science is in demand? History of data science, how data science differs from business intelligence? The lifecycle of data science, data science components, why data science is important? Challenges of data science, tools used for data science, benefits and applications of data science.

Keywords: Data science, history, lifecycle, components, tools

1.1 What is Data Science? What Does a Data Scientist Do?

Data is very important component of any organization. According to International Data Corporation, by 2025, global data will reach to 175 zettabytes. They need data to help them make careful decisions in business. Data is worthless until it is transformed into valuable data. Data science plays a vital role in processing and interpreting data. It focuses on the analysis and management of data too. It is concerned with obtaining useful information from large datasets. It is frequently applied in a wide range of industries, including healthcare, marketing, banking, finance, policy work, and more. This enables companies to make informed decisions around growth, optimization, and performance. In nutshell, Data science is an integrative strategy for deriving actionable insights from today's organizations' massive and ever-increasing data sets. Preparing data for analysis and processing, performing advanced data analysis, and presenting the findings to expose trends and allow stakeholders to make educated decisions are all part of data science [1, 2]. Data science experts are both well-known, data-driven individuals with advanced technical capabilities who can construct complicated quantitative algorithms to organize and interpret huge amounts of data in order to address questions and drive strategy in their company. This is combined with the communication and leadership skills required to provide tangible results to numerous stake-holders throughout a company or organization. Data scientists must be inquisitive and results-driven, with great industry-specific expertise and communication abilities that enable them to convey highly technical outcomes to non-technical colleagues. To create and analyze algorithms, they have a solid quantitative background in statistics and linear algebra, as well as programming experience with a focus on data warehousing, mining, and modeling [3].

1.2 Why Data Science is in Demand?

Data science is the branch of science concerned with the discovery, analysis, modeling, and extraction of useful information which has become a buzz in a lot of companies. Firms are increasingly aware that they have been sitting on data treasure mines the priority with which this data must be analyzed, and ROI generated is obvious. We look at the most important reasons that data science professions are in high demand [4].

• Data Organization

  During the mid-2000s IT boom, the emphasis was on "lifting and shifting" offline business operations into automated computer systems. Digital content generation, transactional data processing, and data log streams have all been consistent throughout the last two decades. This indicates that every company now has a plethora of information that it believes can really be valuable but does not know how to use. This is apparent in Glassdoor's recent analysis, which identifies the 50 greatest jobs in modern era.

• Scarcity of Trained Manpower

  According to a McKinsey Global Institute study, by 2018, the United States will be short 190,000 data scientists, 1.5 million managers, including analysts who would properly comprehend and make judgments based on Big Data. The need is particularly great in India, where the tools and techniques are available but there are not enough qualified people. Data scientists, who can perform analytics, and analytics consultants, who can analyze and apply data, are two sorts of talent shortages, according to Srikanth Velamakanni, co-founder and CEO of Fractal Analytics. The supply of talent in these fields, particularly data scientists, is extremely limited, and the demand is enormous."

• The Pay Is Outstanding

  A data science position is currently one of the highest paying in the market. The national average income for a data scientist/analyst in the United States, according to Glass Door, is more than $62,000. In India, pay is heavily influenced by experience. Those with the appropriate skillset can earn up to 19 LPA. (source: PayScale.)

• The "X" Factor

  A data scientist's major responsibility are exceptional and specific to the position. Because of nature of the profession, they may flourish in their careers by integrating several analytical expertise across diverse areas such as big data, machine learning, and so on. This vast knowledge base gives them an unsurpassed reputation or X-factor.

• Data Scientists' Democratization

  Tech behemoths are not the only ones who need data scientists. According to a Harvard Business Report issued many years ago, "Organizations in the top list of their area in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their peers". Even mid-sized and small organizations have been driven to adopt data science because of this. In truth, many small businesses are trying to hire entry-level data scientists for a fair wage. This works well for both. The scientist will be able to further develop his or her skills, and the company will be able to pay less than it would otherwise.

• Fewer Barriers for Professionals

  Data science is open to a wide range of experts from varied backgrounds because it is a relatively new discipline. Math/statistics, computer science and engineering, and natural science are all areas of knowledge for today's data scientists. Some perhaps have social science, economics, or business degrees. They have all devised a problem-solving technique and improved their skills through formal or online education.

• Abundance of Jobs

  Data science is employed in a wide range of business sectors, from production to healthcare, Information Technology to finance, therefore there are plenty of data science jobs available for individuals who are interested and willing to put in the effort. It is true not only in terms of industries, but also in terms of geography. So, regardless of one's geographical location or current domain, data science and analytics are available to everybody.

• A Wide Range of Roles

  Even if data science job is indeed a broad term, there are numerous subroles that fall under its scope. Data scientists, data architects, business intelligence engineers, business analysts, data engineers, database administrators, and data analytics managers are all in considerable demand.

1.3 History of Data Science

The terminology "data science" was just recently coined a new profession interested in trying to make sense of large volumes of data. Making sense of data, on the other hand, has a significant background, and it has been addressed for years by many computer scientists, scientists, librarians, statisticians, and others. The history below shows how the terminology: data science" evolved over time, as well as attempts to describe it and associated concepts [5].

In 1974, Peter Naur's book gives a broad overview of modern data processing techniques that are employed in a variety of applications. The IFIP Guide to Data Processing Concepts and Terms states that it is organized around the data principle: "Data is a codified representation of ideas or facts that may be communicated and even perhaps changed by certain process." According to the book's preface, a course plan titled "Datalogy, the science of data and data processes, and its position in education" was presented at the 1968 IFIP Congress, and the name "data science" has been widely used since then. Data science, according to Naur, is defined as "the science of working with data after it has been established, but the relationship of the data to what it represents is assigned to other disciplines and sciences."

In 1977, the International Association for Statistical Computing (IASC) was founded as an ISI chapter. "The goal of the IASC is to connect conventional statistical techniques, innovative computer technology, and domain specialists' skills to transform data into...